Mobile device operation tasks are increasingly becoming a popular multi-modal
AI application scenario. Current Multi-modal Large Language Models (MLLMs),
constrained by their training data, lack the capability to function effectively
as operation assistants. Instead, MLLM-based agents, which enhance capabilities
through tool invocation, are gradually being applied to this scenario. However,
the two major navigation challenges in mobile device operation tasks, task
progress navigation and focus content navigation, are significantly complicated
under the single-agent architecture of existing work. This is due to the overly
long token sequences and the interleaved text-image data format, which limit
performance. To address these navigation challenges effectively, we propose
Mobile-Agent-v2, a multi-agent architecture for mobile device operation
assistance. The architecture comprises three agents: planning agent, decision
agent, and reflection agent. The planning agent generates task progress, making
the navigation of history operations more efficient. To retain focus content,
we design a memory unit that updates with task progress. Additionally, to
correct erroneous operations, the reflection agent observes the outcomes of
each operation and handles any mistakes accordingly. Experimental results
indicate that Mobile-Agent-v2 achieves over a 30% improvement in task
completion compared to the single-agent architecture of Mobile-Agent. The code
is open-sourced at this https URL

通过多代理体系架构，Mobile-Agent-v2 为移动设备操作任务提供了较好的导航能力，相比于单代理架构的 Mobile-Agent，在任务完成方面取得了 30% 以上的改进。

Mobile-Agent-v2: 移动设备操作助手通过多智能体协作实现有效导航

Mobile-Agent-v2: Mobile Device Operation Assistant with Effective  Navigation via Multi-Agent Collaboration

Fairness problems in recommender systems often have a complexity in practice
that is not adequately captured in simplified research formulations. A social
choice formulation of the fairness problem, operating within a multi-agent
architecture of fairness concerns, offers a flexible and multi-aspect
alternative to fairness-aware recommendation approaches. Leveraging social
choice allows for increased generality and the possibility of tapping into
well-studied social choice algorithms for resolving the tension between
multiple, competing fairness concerns. This paper explores a range of options
for choice mechanisms in multi-aspect fairness applications using both real and
synthetic data and shows that different classes of choice and allocation
mechanisms yield different but consistent fairness / accuracy tradeoffs. We
also show that a multi-agent formulation offers flexibility in adapting to user
population dynamics.

在社交选择公正问题的多代理体系结构下，研究探索了多方面公正应用中的多种选择机制，发现不同类别的选择和分配机制在公正 / 准确性权衡方面产生了不同但一致的结果，并展示多代理体系结构在适应用户人口动态方面具有灵活性。